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Abstract

A model training method based on adaptive split learning-federated learning includes: each user terminal uploading device information to the server and the server allocating a propagation step length and a aggregation weight to each user terminal; each user terminal obtaining a current-round global model from the server and taking itself as a start node of a ring topology to perform local joint processing for a preset number of times to obtain a locally-updated model parameter of the start node with respect to current-round training; each user terminal uploading the locally-updated model parameter for the current-round training to the server for aggregation and obtaining a current-round updated global model; and the server determining whether the current-round updated global model meets a convergence condition, if not, performing next-round training, or if yes, determining the current-round updated global model as a trained face recognition model.

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First Claim